Robust multi-view learning via adaptive regression

As data collected from different sources have multiple representations, multi-view learning has become an important paradigm of machine learning . To exploit multi-view data, previous works either tackle each view separately or concatenate all views directly, such that the distinctions as well as correlations of different views are often ignored. Furthermore, existing models usually involve intractable parameters that need to be manually determined to balance the contributions of different views, degrading the efficiency and applicability of models. In this paper, a novel multi-view learning framework, namely Robust Multi-view learning via Adaptive Regression (RMAR), is derived to discriminate diverse views in a self-supervised weighting manner without extra parameters. Meanwhile, RMAR coalesces multiple feature projections with adaptive view-wise weights and adopts L 2 , 1 -norm regression loss to learn a joint projection subspace compatible across all views, not only increasing the robustness of model but also preserving the consistency and diversity among views. Furthermore, RMAR can be naturally extended for feature selection by imposing L 2 , 1 -norm constraint on feature projections. Additionally, an efficient convergent algorithm is developed to solve RMAR. Extensive experiments have been performed to validate the effectiveness of RMAR for classification and feature selection and show its superiority over state-of-the-arts.